Intelligent planning and control

Construction of logical-transformational rules for situational control of complex objects under undetermined operating conditions

V. B. Melekhin^a, M. V. Khachumov<sup>bcd

a</sup> Daghestan State Technical University, Makhachkala, Russia
^b Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
^c Ailamazyan Program Systems Institute of Russian Academy of Sciences, Pereslavl-Zalesskiy, Russia
^d Peoples' Friendship University of Russia named after Patrice Lumumba, Moscow, Russia

Abstract: The paper proposes an original approach to organizing situational management of complex objects in underdetermined environmental conditions. A characteristic feature of this approach is the ability to construct logical-transformational rules that are lacking for managing a complex object in a priori unforeseen operating conditions. To solve this problem, typical elements of an auxiliary model of knowledge representation and instrumental means of their processing in the process of constructing logical-transformational rules necessary for determining effective control actions on a complex object in newly discovered a priori unforeseen environmental circumstances have been developed. The conditions are considered that allow, in the process of inferring control decisions, to classify current problem situations that have arisen at the control object into one of the classes of problem situations, respectively, determined by semantic and active fuzzy semantic networks. This allows for an unambiguous choice of logical-transformational rules that include in the structure effective control actions on the control object related to the elimination of problem situations arising on it and to organize multi-step situational control of its purposeful activity in continuous time. The conclusion shows the prospects for further development of the results obtained in the article.

Keywords: complex object, situational control, semantic network, knowledge representation model, decision making.

DOI: 10.14357/20718594250309